



'''simple docstring'''
import os
from collections.abc import Iterator



def        __A   (							_SCREAMING_SNAKE_CASE     :  List[Any] = "."	):







					"""simple docstring"""






					for dir_path, dir_names, filenames in os.walk(a__	):
										__SCREAMING_SNAKE_CASE				:    Dict						    =							[d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
										for filename in filenames:
															if filename == "__init__.py":
																				continue
															if os.path.splitext(a__	)[1] in (".py", ".ipynb"):
																				yield os.path.join(a__					,   a__	).lstrip("./"	)



def        __A   (							_SCREAMING_SNAKE_CASE     :  Any	):







					"""simple docstring"""






					return f'{i * "  "}*' if i else "\n##"



def        __A   (							_SCREAMING_SNAKE_CASE     :  Optional[Any]					,   _SCREAMING_SNAKE_CASE     :  Dict	):







					"""simple docstring"""






					__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							old_path.split(os.sep	)
					for i, new_part in enumerate(new_path.split(os.sep	)	):
										if (i + 1 > len(a__	) or old_parts[i] != new_part) and new_part:
															print(f'{md_prefix(a__	)} {new_part.replace("_"					,   " "	).title()}'	)
					return new_path



def        __A   (							_SCREAMING_SNAKE_CASE     :  Dict = "."	):







					"""simple docstring"""






					__SCREAMING_SNAKE_CASE				:    List[str]						    =							""""""
					for filepath in sorted(good_file_paths(a__	)	):
										__SCREAMING_SNAKE_CASE				:    Tuple						    =							os.path.split(a__	)
										if filepath != old_path:
															__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							print_path(a__					,   a__	)
										__SCREAMING_SNAKE_CASE				:    int						    =							(filepath.count(os.sep	) + 1) if filepath else 0
										__SCREAMING_SNAKE_CASE				:    Dict						    =							f'{filepath}/{filename}'.replace(" "					,   "%20"	)
										__SCREAMING_SNAKE_CASE				:    Dict						    =							os.path.splitext(filename.replace("_"					,   " "	).title()	)[0]
										print(f'{md_prefix(a__	)} [{filename}]({url})'	)


if __name__ == "__main__":
					print_directory_md('''.''')





import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
	    FlaxRobertaPreLayerNormForCausalLM,
	    FlaxRobertaPreLayerNormForMaskedLM,
	    FlaxRobertaPreLayerNormForMultipleChoice,
	    FlaxRobertaPreLayerNormForQuestionAnswering,
	    FlaxRobertaPreLayerNormForSequenceClassification,
	    FlaxRobertaPreLayerNormForTokenClassification,
	    FlaxRobertaPreLayerNormModel,
	)



class        A__(unittest.TestCase							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=13  ,      _lowercase=7  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=99  ,      _lowercase=32  ,      _lowercase=5  ,      _lowercase=4  ,      _lowercase=37  ,      _lowercase="gelu"  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=512  ,      _lowercase=16  ,      _lowercase=2  ,      _lowercase=0.0_2  ,      _lowercase=4  ,      )     -> Tuple:
									a_						:						Tuple 							= parent
									a_						:						Optional[Any] 							= batch_size
									a_						:						int 							= seq_length
									a_						:						List[str] 							= is_training
									a_						:						str 							= use_attention_mask
									a_						:						Optional[int] 							= use_token_type_ids
									a_						:						Optional[int] 							= use_labels
									a_						:						int 							= vocab_size
									a_						:						Union[str, Any] 							= hidden_size
									a_						:						Optional[int] 							= num_hidden_layers
									a_						:						Optional[int] 							= num_attention_heads
									a_						:						Tuple 							= intermediate_size
									a_						:						int 							= hidden_act
									a_						:						Any 							= hidden_dropout_prob
									a_						:						int 							= attention_probs_dropout_prob
									a_						:						List[str] 							= max_position_embeddings
									a_						:						Optional[int] 							= type_vocab_size
									a_						:						List[str] 							= type_sequence_label_size
									a_						:						Optional[Any] 							= initializer_range
									a_						:						Optional[int] 							= num_choices





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						int 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size    )

									a_						:						Dict 							= None
									if self.use_attention_mask:
												a_						:						List[str] 							= random_attention_mask([self.batch_size, self.seq_length]    )

									a_						:						List[str] 							= None
									if self.use_token_type_ids:
												a_						:						Dict 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.type_vocab_size    )

									a_						:						Any 							= RobertaPreLayerNormConfig(
									    vocab_size=self.vocab_size  ,      hidden_size=self.hidden_size  ,      num_hidden_layers=self.num_hidden_layers  ,      num_attention_heads=self.num_attention_heads  ,      intermediate_size=self.intermediate_size  ,      hidden_act=self.hidden_act  ,      hidden_dropout_prob=self.hidden_dropout_prob  ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,      max_position_embeddings=self.max_position_embeddings  ,      type_vocab_size=self.type_vocab_size  ,      is_decoder=_lowercase  ,      initializer_range=self.initializer_range  ,      )

									return config, input_ids, token_type_ids, attention_mask





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Optional[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						Any 							= config_and_inputs
									a_						:						List[Any] 							= {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
									return config, inputs_dict





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Optional[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						str 							= config_and_inputs

									a_						:						str 							= True
									a_						:						Any 							= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
									a_						:						Tuple 							= ids_tensor([self.batch_size, self.seq_length]  ,      vocab_size=2    )

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				List[str]       					=			True

						_A    :				int       					=			(
						    (
						        FlaxRobertaPreLayerNormModel,
						        FlaxRobertaPreLayerNormForCausalLM,
						        FlaxRobertaPreLayerNormForMaskedLM,
						        FlaxRobertaPreLayerNormForSequenceClassification,
						        FlaxRobertaPreLayerNormForTokenClassification,
						        FlaxRobertaPreLayerNormForMultipleChoice,
						        FlaxRobertaPreLayerNormForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Optional[int] 							= FlaxRobertaPreLayerNormModelTester(self    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> int:
									for model_class_name in self.all_model_classes:
												a_						:						Union[str, Any] 							= model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )
												a_						:						Union[str, Any] 							= model(np.ones((1, 1)    )    )
												self.assertIsNotNone(_lowercase    )







@require_flax
class        A__(unittest.TestCase							):
						"""simple docstring"""






						@slow
						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Union[str, Any] 							= FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )

									a_						:						Union[str, Any] 							= np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  ,      dtype=jnp.intaa    )
									a_						:						Tuple 							= model(_lowercase    )[0]
									a_						:						Tuple 							= [1, 11, 50_265]
									self.assertEqual(list(output.shape    )  ,      _lowercase    )
									# compare the actual values for a slice.
									a_						:						Tuple 							= np.array(
									    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]  ,      dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]  ,      _lowercase  ,      atol=1e-4    )    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						Union[str, Any] 							= FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )

									a_						:						List[str] 							= np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  ,      dtype=jnp.intaa    )
									a_						:						str 							= model(_lowercase    )[0]
									# compare the actual values for a slice.
									a_						:						Union[str, Any] 							= np.array(
									    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]  ,      dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]  ,      _lowercase  ,      atol=1e-4    )    )


